Global convergence conditions in maximum likelihood estimation
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Maximum likelihood estimation has been widely applied in system identification because of consistency, its asymptotic efficiency and sufficiency. However, gradient-based optimisation of the likelihood function might end up in local convergence. In this article we derive various new non-local-minimum conditions in both open and closed-loop system when the noise distribution is a Gaussian process. Here we consider different model structures, in particular ARARMAX, BJ and OE models
| Original language | Unknown |
|---|---|
| Pages (from-to) | 475-490 |
| Number of pages | 16 |
| Journal | International Journal of Control |
| Volume | 85 |
| Issue number | 5 |
| Early online date | 7 Feb 2012 |
| DOIs | |
| Publication status | E-pub ahead of print - 7 Feb 2012 |
| Externally published | Yes |